





class 					A__       :




   """simple docstring"""



   def __init__(   self   ,    lowercase = ""   ,    lowercase = False)     ->		None:







          '''simple docstring'''

          a__		:      dict[str, RadixNode]					=							{}

          # A node will be a leaf if the tree contains its word
          a__		:      Tuple					=							is_leaf

          a__		:      Tuple					=							prefix

   def 				__lowercase						(   self   ,    lowercase)     ->		tuple[str, str, str]:







          '''simple docstring'''

          a__		:      Dict					=							0
          for q, w in zip(self.prefix   ,    lowercase):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]

   def 				__lowercase						(   self   ,    lowercase)     ->		None:







          '''simple docstring'''

          for word in words:
                 self.insert(lowercase)

   def 				__lowercase						(   self   ,    lowercase)     ->		None:







          '''simple docstring'''

          if self.prefix == word:
                 a__		:      Dict					=							True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 a__		:      Any					=							RadixNode(prefix=lowercase   ,    is_leaf=lowercase)

          else:
                 a__		:      str					=							self.nodes[word[0]]
                 a__		:      List[Any]					=							incoming_node.match(
                     lowercase)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(lowercase)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        a__		:      Union[str, Any]					=							remaining_prefix

                        a__		:      Union[str, Any]					=							self.nodes[matching_string[0]]
                        a__		:      List[Any]					=							RadixNode(lowercase   ,    lowercase)
                        a__		:      Optional[int]					=							aux_node

                        if remaining_word == "":
                               a__		:      Optional[Any]					=							True
                        else:
                               self.nodes[matching_string[0]].insert(lowercase)

   def 				__lowercase						(   self   ,    lowercase)     ->		bool:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							self.nodes.get(word[0]   ,    lowercase)
          if not incoming_node:
                 return False
          else:
                 a__		:      List[str]					=							incoming_node.match(
                     lowercase)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(lowercase)

   def 				__lowercase						(   self   ,    lowercase)     ->		bool:







          '''simple docstring'''

          a__		:      Optional[Any]					=							self.nodes.get(word[0]   ,    lowercase)
          if not incoming_node:
                 return False
          else:
                 a__		:      Any					=							incoming_node.match(
                     lowercase)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(lowercase)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes) == 1 and not self.is_leaf:
                                             a__		:      str					=							list(self.nodes.values())[0]
                                             a__		:      Union[str, Any]					=							merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             a__		:      Dict					=							merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes) > 1:
                                      a__		:      int					=							False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      a__		:      Tuple					=							list(incoming_node.nodes.values())[0]
                                      a__		:      List[str]					=							merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      a__		:      Union[str, Any]					=							merging_node.nodes

                               return True

   def 				__lowercase						(   self   ,    lowercase = 0)     ->		None:







          '''simple docstring'''

          if self.prefix != "":
                 print('-' * height   ,    self.prefix   ,    '  (leaf)' if self.is_leaf else '')

          for value in self.nodes.values():
                 value.print_tree(height + 1)



def A_      (						)    ->						bool:
       a__		:      Union[str, Any]					=							"banana bananas bandana band apple all beast".split()
       a__		:      Tuple					=							RadixNode()
       root.insert_many(_lowerCamelCase							)

       assert all(root.find(_lowerCamelCase							) for word in words							)
       assert not root.find('bandanas'							)
       assert not root.find('apps'							)
       root.delete('all'							)
       assert not root.find('all'							)
       root.delete('banana'							)
       assert not root.find('banana'							)
       assert root.find('bananas'							)

       return True



def A_      (						)    ->						None:
       assert test_trie()



def A_      (						)    ->						None:
       a__		:      List[Any]					=							RadixNode()
       a__		:      List[Any]					=							"banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(_lowerCamelCase							)

       print('Words:'						, _lowerCamelCase							)
       print('Tree:'							)
       root.print_tree()


if __name__ == "__main__":
    main()




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)